Heart attack prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "581989fc-9766-4713-9e4c-0a87257d229d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "from sklearn.naive_bayes import GaussianNB,BernoulliNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "232a79d4-e7cb-4a2f-8c09-c15f07cb382e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#About The Dataset :\n",
    "\n",
    "#age: Age of the patient\n",
    "\n",
    "#sex: Sex of the patient\n",
    "\n",
    "#cp: Chest pain type, 0 = Typical Angina, 1 = Atypical Angina, 2 = Non-anginal Pain, 3 = Asymptomatic\n",
    "\n",
    "#trtbps: Resting blood pressure (in mm Hg)\n",
    "\n",
    "#chol: Cholestoral in mg/dl fetched via BMI sensor\n",
    "\n",
    "#fbs: (fasting blood sugar > 120 mg/dl), 1 = True, 0 = False\n",
    "\n",
    "#restecg: Resting electrocardiographic results, 0 = Normal, 1 = ST-T wave normality, 2 = Left ventricular hypertrophy\n",
    "\n",
    "#thalachh: Maximum heart rate achieved\n",
    "\n",
    "#oldpeak: Previous peak\n",
    "\n",
    "#slp: Slope\n",
    "\n",
    "#caa: Number of major vessels\n",
    "\n",
    "#thall: Thalium Stress Test result ~ (0,3)\n",
    "\n",
    "#exng: Exercise induced angina ~ 1 = Yes, 0 = No\n",
    "\n",
    "#output: Target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1602e9e-23c3-4bad-ab28-0efcd8da0e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>resting bp s</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>resting ecg</th>\n",
       "      <th>max heart rate</th>\n",
       "      <th>exercise angina</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ST slope</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  chest pain type  resting bp s  cholesterol  fasting blood sugar  \\\n",
       "0   40    1                2           140          289                    0   \n",
       "1   49    0                3           160          180                    0   \n",
       "2   37    1                2           130          283                    0   \n",
       "3   48    0                4           138          214                    0   \n",
       "4   54    1                3           150          195                    0   \n",
       "\n",
       "   resting ecg  max heart rate  exercise angina  oldpeak  ST slope  target  \n",
       "0            0             172                0      0.0         1       0  \n",
       "1            0             156                0      1.0         2       1  \n",
       "2            1              98                0      0.0         1       0  \n",
       "3            0             108                1      1.5         2       1  \n",
       "4            0             122                0      0.0         1       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart=pd.read_csv(r'C:\\Users\\Public\\Datasets\\heart_statlog_cleveland_hungary_final.csv')\n",
    "heart.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cec92bfa-3c4e-4730-954a-b2a24bd55ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows are 1190 and number of columns are  12\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows are',heart.shape[0], 'and number of columns are ',heart.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5acac39-9af4-4bc7-807b-547ca42755ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                    0.0\n",
       "sex                    0.0\n",
       "chest pain type        0.0\n",
       "resting bp s           0.0\n",
       "cholesterol            0.0\n",
       "fasting blood sugar    0.0\n",
       "resting ecg            0.0\n",
       "max heart rate         0.0\n",
       "exercise angina        0.0\n",
       "oldpeak                0.0\n",
       "ST slope               0.0\n",
       "target                 0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.isnull().sum()/len(heart)*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d00cfe2c-0b1a-4286-a781-cfdbc894e5a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1190 entries, 0 to 1189\n",
      "Data columns (total 12 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   age                  1190 non-null   int64  \n",
      " 1   sex                  1190 non-null   int64  \n",
      " 2   chest pain type      1190 non-null   int64  \n",
      " 3   resting bp s         1190 non-null   int64  \n",
      " 4   cholesterol          1190 non-null   int64  \n",
      " 5   fasting blood sugar  1190 non-null   int64  \n",
      " 6   resting ecg          1190 non-null   int64  \n",
      " 7   max heart rate       1190 non-null   int64  \n",
      " 8   exercise angina      1190 non-null   int64  \n",
      " 9   oldpeak              1190 non-null   float64\n",
      " 10  ST slope             1190 non-null   int64  \n",
      " 11  target               1190 non-null   int64  \n",
      "dtypes: float64(1), int64(11)\n",
      "memory usage: 111.7 KB\n"
     ]
    }
   ],
   "source": [
    "heart.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c52766f7-e67d-4fe3-a7b0-c89fafd90165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>resting bp s</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>resting ecg</th>\n",
       "      <th>max heart rate</th>\n",
       "      <th>exercise angina</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ST slope</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "      <td>208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>150</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>286</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1157</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>311</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  sex  chest pain type  resting bp s  cholesterol  \\\n",
       "163    49    0                2           110          208   \n",
       "604    58    1                3           150          219   \n",
       "887    63    1                1           145          233   \n",
       "888    67    1                4           160          286   \n",
       "889    67    1                4           120          229   \n",
       "...   ...  ...              ...           ...          ...   \n",
       "1156   42    1                3           130          180   \n",
       "1157   61    1                4           140          207   \n",
       "1158   66    1                4           160          228   \n",
       "1159   46    1                4           140          311   \n",
       "1160   71    0                4           112          149   \n",
       "\n",
       "      fasting blood sugar  resting ecg  max heart rate  exercise angina  \\\n",
       "163                     0            0             160                0   \n",
       "604                     0            1             118                1   \n",
       "887                     1            2             150                0   \n",
       "888                     0            2             108                1   \n",
       "889                     0            2             129                1   \n",
       "...                   ...          ...             ...              ...   \n",
       "1156                    0            0             150                0   \n",
       "1157                    0            2             138                1   \n",
       "1158                    0            2             138                0   \n",
       "1159                    0            0             120                1   \n",
       "1160                    0            0             125                0   \n",
       "\n",
       "      oldpeak  ST slope  target  \n",
       "163       0.0         1       0  \n",
       "604       0.0         2       1  \n",
       "887       2.3         3       0  \n",
       "888       1.5         2       1  \n",
       "889       2.6         2       1  \n",
       "...       ...       ...     ...  \n",
       "1156      0.0         1       0  \n",
       "1157      1.9         1       1  \n",
       "1158      2.3         1       0  \n",
       "1159      1.8         2       1  \n",
       "1160      1.6         2       0  \n",
       "\n",
       "[272 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart[heart.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec415491-cad9-41ee-b86e-d3f599048fd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "heart.drop_duplicates(keep='first',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3cc9141-06f1-4b98-bfa7-2227fa4b0717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows are 918 and number of columns are  12\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows are',heart.shape[0], 'and number of columns are ',heart.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "06d88267-7d1e-4233-9fea-d7b54f1ccf11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>resting bp s</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>resting ecg</th>\n",
       "      <th>max heart rate</th>\n",
       "      <th>exercise angina</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ST slope</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>0.789760</td>\n",
       "      <td>3.251634</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>0.603486</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.404139</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>1.636166</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>0.407701</td>\n",
       "      <td>0.931031</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>0.490992</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.609341</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex  chest pain type  resting bp s  cholesterol  \\\n",
       "count  918.000000  918.000000       918.000000    918.000000   918.000000   \n",
       "mean    53.510893    0.789760         3.251634    132.396514   198.799564   \n",
       "std      9.432617    0.407701         0.931031     18.514154   109.384145   \n",
       "min     28.000000    0.000000         1.000000      0.000000     0.000000   \n",
       "25%     47.000000    1.000000         3.000000    120.000000   173.250000   \n",
       "50%     54.000000    1.000000         4.000000    130.000000   223.000000   \n",
       "75%     60.000000    1.000000         4.000000    140.000000   267.000000   \n",
       "max     77.000000    1.000000         4.000000    200.000000   603.000000   \n",
       "\n",
       "       fasting blood sugar  resting ecg  max heart rate  exercise angina  \\\n",
       "count           918.000000   918.000000      918.000000       918.000000   \n",
       "mean              0.233115     0.603486      136.809368         0.404139   \n",
       "std               0.423046     0.805968       25.460334         0.490992   \n",
       "min               0.000000     0.000000       60.000000         0.000000   \n",
       "25%               0.000000     0.000000      120.000000         0.000000   \n",
       "50%               0.000000     0.000000      138.000000         0.000000   \n",
       "75%               0.000000     1.000000      156.000000         1.000000   \n",
       "max               1.000000     2.000000      202.000000         1.000000   \n",
       "\n",
       "          oldpeak    ST slope      target  \n",
       "count  918.000000  918.000000  918.000000  \n",
       "mean     0.887364    1.636166    0.553377  \n",
       "std      1.066570    0.609341    0.497414  \n",
       "min     -2.600000    0.000000    0.000000  \n",
       "25%      0.000000    1.000000    0.000000  \n",
       "50%      0.600000    2.000000    1.000000  \n",
       "75%      1.500000    2.000000    1.000000  \n",
       "max      6.200000    3.000000    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d07c532-8600-4b3a-b8fa-72361d72fb6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>chest pain type</th>\n",
       "      <th>resting bp s</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <th>resting ecg</th>\n",
       "      <th>max heart rate</th>\n",
       "      <th>exercise angina</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ST slope</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055750</td>\n",
       "      <td>0.165896</td>\n",
       "      <td>0.254399</td>\n",
       "      <td>-0.095282</td>\n",
       "      <td>0.198039</td>\n",
       "      <td>0.213152</td>\n",
       "      <td>-0.382045</td>\n",
       "      <td>0.215793</td>\n",
       "      <td>0.258612</td>\n",
       "      <td>0.261760</td>\n",
       "      <td>0.282039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.055750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168254</td>\n",
       "      <td>0.005133</td>\n",
       "      <td>-0.200092</td>\n",
       "      <td>0.120076</td>\n",
       "      <td>-0.018343</td>\n",
       "      <td>-0.189186</td>\n",
       "      <td>0.190664</td>\n",
       "      <td>0.105734</td>\n",
       "      <td>0.148282</td>\n",
       "      <td>0.305445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chest pain type</th>\n",
       "      <td>0.165896</td>\n",
       "      <td>0.168254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022168</td>\n",
       "      <td>-0.136139</td>\n",
       "      <td>0.116703</td>\n",
       "      <td>0.031383</td>\n",
       "      <td>-0.343654</td>\n",
       "      <td>0.416625</td>\n",
       "      <td>0.245027</td>\n",
       "      <td>0.317256</td>\n",
       "      <td>0.471354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resting bp s</th>\n",
       "      <td>0.254399</td>\n",
       "      <td>0.005133</td>\n",
       "      <td>0.022168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100893</td>\n",
       "      <td>0.070193</td>\n",
       "      <td>0.097661</td>\n",
       "      <td>-0.112135</td>\n",
       "      <td>0.155101</td>\n",
       "      <td>0.164803</td>\n",
       "      <td>0.071477</td>\n",
       "      <td>0.107589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cholesterol</th>\n",
       "      <td>-0.095282</td>\n",
       "      <td>-0.200092</td>\n",
       "      <td>-0.136139</td>\n",
       "      <td>0.100893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.260974</td>\n",
       "      <td>0.112095</td>\n",
       "      <td>0.235792</td>\n",
       "      <td>-0.034166</td>\n",
       "      <td>0.050148</td>\n",
       "      <td>-0.110928</td>\n",
       "      <td>-0.232741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fasting blood sugar</th>\n",
       "      <td>0.198039</td>\n",
       "      <td>0.120076</td>\n",
       "      <td>0.116703</td>\n",
       "      <td>0.070193</td>\n",
       "      <td>-0.260974</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050707</td>\n",
       "      <td>-0.131438</td>\n",
       "      <td>0.060451</td>\n",
       "      <td>0.052698</td>\n",
       "      <td>0.168627</td>\n",
       "      <td>0.267291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resting ecg</th>\n",
       "      <td>0.213152</td>\n",
       "      <td>-0.018343</td>\n",
       "      <td>0.031383</td>\n",
       "      <td>0.097661</td>\n",
       "      <td>0.112095</td>\n",
       "      <td>0.050707</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048552</td>\n",
       "      <td>0.036119</td>\n",
       "      <td>0.114428</td>\n",
       "      <td>0.081191</td>\n",
       "      <td>0.061011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max heart rate</th>\n",
       "      <td>-0.382045</td>\n",
       "      <td>-0.189186</td>\n",
       "      <td>-0.343654</td>\n",
       "      <td>-0.112135</td>\n",
       "      <td>0.235792</td>\n",
       "      <td>-0.131438</td>\n",
       "      <td>0.048552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.370425</td>\n",
       "      <td>-0.160691</td>\n",
       "      <td>-0.341455</td>\n",
       "      <td>-0.400421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exercise angina</th>\n",
       "      <td>0.215793</td>\n",
       "      <td>0.190664</td>\n",
       "      <td>0.416625</td>\n",
       "      <td>0.155101</td>\n",
       "      <td>-0.034166</td>\n",
       "      <td>0.060451</td>\n",
       "      <td>0.036119</td>\n",
       "      <td>-0.370425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.408752</td>\n",
       "      <td>0.430044</td>\n",
       "      <td>0.494282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.258612</td>\n",
       "      <td>0.105734</td>\n",
       "      <td>0.245027</td>\n",
       "      <td>0.164803</td>\n",
       "      <td>0.050148</td>\n",
       "      <td>0.052698</td>\n",
       "      <td>0.114428</td>\n",
       "      <td>-0.160691</td>\n",
       "      <td>0.408752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.503017</td>\n",
       "      <td>0.403951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST slope</th>\n",
       "      <td>0.261760</td>\n",
       "      <td>0.148282</td>\n",
       "      <td>0.317256</td>\n",
       "      <td>0.071477</td>\n",
       "      <td>-0.110928</td>\n",
       "      <td>0.168627</td>\n",
       "      <td>0.081191</td>\n",
       "      <td>-0.341455</td>\n",
       "      <td>0.430044</td>\n",
       "      <td>0.503017</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.553461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.282039</td>\n",
       "      <td>0.305445</td>\n",
       "      <td>0.471354</td>\n",
       "      <td>0.107589</td>\n",
       "      <td>-0.232741</td>\n",
       "      <td>0.267291</td>\n",
       "      <td>0.061011</td>\n",
       "      <td>-0.400421</td>\n",
       "      <td>0.494282</td>\n",
       "      <td>0.403951</td>\n",
       "      <td>0.553461</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          age       sex  chest pain type  resting bp s  \\\n",
       "age                  1.000000  0.055750         0.165896      0.254399   \n",
       "sex                  0.055750  1.000000         0.168254      0.005133   \n",
       "chest pain type      0.165896  0.168254         1.000000      0.022168   \n",
       "resting bp s         0.254399  0.005133         0.022168      1.000000   \n",
       "cholesterol         -0.095282 -0.200092        -0.136139      0.100893   \n",
       "fasting blood sugar  0.198039  0.120076         0.116703      0.070193   \n",
       "resting ecg          0.213152 -0.018343         0.031383      0.097661   \n",
       "max heart rate      -0.382045 -0.189186        -0.343654     -0.112135   \n",
       "exercise angina      0.215793  0.190664         0.416625      0.155101   \n",
       "oldpeak              0.258612  0.105734         0.245027      0.164803   \n",
       "ST slope             0.261760  0.148282         0.317256      0.071477   \n",
       "target               0.282039  0.305445         0.471354      0.107589   \n",
       "\n",
       "                     cholesterol  fasting blood sugar  resting ecg  \\\n",
       "age                    -0.095282             0.198039     0.213152   \n",
       "sex                    -0.200092             0.120076    -0.018343   \n",
       "chest pain type        -0.136139             0.116703     0.031383   \n",
       "resting bp s            0.100893             0.070193     0.097661   \n",
       "cholesterol             1.000000            -0.260974     0.112095   \n",
       "fasting blood sugar    -0.260974             1.000000     0.050707   \n",
       "resting ecg             0.112095             0.050707     1.000000   \n",
       "max heart rate          0.235792            -0.131438     0.048552   \n",
       "exercise angina        -0.034166             0.060451     0.036119   \n",
       "oldpeak                 0.050148             0.052698     0.114428   \n",
       "ST slope               -0.110928             0.168627     0.081191   \n",
       "target                 -0.232741             0.267291     0.061011   \n",
       "\n",
       "                     max heart rate  exercise angina   oldpeak  ST slope  \\\n",
       "age                       -0.382045         0.215793  0.258612  0.261760   \n",
       "sex                       -0.189186         0.190664  0.105734  0.148282   \n",
       "chest pain type           -0.343654         0.416625  0.245027  0.317256   \n",
       "resting bp s              -0.112135         0.155101  0.164803  0.071477   \n",
       "cholesterol                0.235792        -0.034166  0.050148 -0.110928   \n",
       "fasting blood sugar       -0.131438         0.060451  0.052698  0.168627   \n",
       "resting ecg                0.048552         0.036119  0.114428  0.081191   \n",
       "max heart rate             1.000000        -0.370425 -0.160691 -0.341455   \n",
       "exercise angina           -0.370425         1.000000  0.408752  0.430044   \n",
       "oldpeak                   -0.160691         0.408752  1.000000  0.503017   \n",
       "ST slope                  -0.341455         0.430044  0.503017  1.000000   \n",
       "target                    -0.400421         0.494282  0.403951  0.553461   \n",
       "\n",
       "                       target  \n",
       "age                  0.282039  \n",
       "sex                  0.305445  \n",
       "chest pain type      0.471354  \n",
       "resting bp s         0.107589  \n",
       "cholesterol         -0.232741  \n",
       "fasting blood sugar  0.267291  \n",
       "resting ecg          0.061011  \n",
       "max heart rate      -0.400421  \n",
       "exercise angina      0.494282  \n",
       "oldpeak              0.403951  \n",
       "ST slope             0.553461  \n",
       "target               1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Computing the correlation matrix\n",
    "\n",
    "heart.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4d565d6-b29f-4e39-98bd-333ca97cafc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people having sex as 1 are 193 and Number of people having sex as 0 are 725\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data visualization\n",
    "#Breakdown of gender\n",
    "\n",
    "x=(heart.sex.value_counts())\n",
    "print(f'Number of people having sex as 1 are {x[0]} and Number of people having sex as 0 are {x[1]}')\n",
    "p = sns.countplot(data=heart, x=\"sex\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b260e0e2-e956-46e1-b615-2b8baedde867",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data visualization\n",
    "#breakdown of cp\n",
    "x=(heart.cp.value_counts())\n",
    "print(x)\n",
    "p = sns.countplot(data=heart, x=\"cp\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe778c9c-4534-45fe-852c-8405cca5f005",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data visualization\n",
    "#Breakdown of ECG\n",
    "\n",
    "x=(heart.restecg.value_counts())\n",
    "print(x)\n",
    "p = sns.countplot(data=heart, x=\"restecg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b6f0390-de0f-4d27-9708-c7a5e1170276",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data visualization\n",
    "#Breakdown for Exercise Induced Angina\n",
    "\n",
    "x=(heart.exng.value_counts())\n",
    "print(x)\n",
    "p = sns.countplot(data=heart, x=\"exng\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a98c5526-683c-4df3-bf9e-0273e74ecf4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data visualization \n",
    "#Breakdown for Thalium Stress Test\n",
    "\n",
    "x=(heart.thall.value_counts())\n",
    "print(x)\n",
    "p = sns.countplot(data=heart, x=\"thall\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62a2e650-a6af-423d-a72c-28c91e223312",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Density distribution for Age\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.displot(heart.age, color=\"red\", label=\"Age\", kde= True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a9dc40e-32ad-4664-ade9-688888f9cce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Density distribution is highest for age group 55 to 60\n",
    "\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.displot(heart.trtbps , color=\"green\", label=\"Resting Blood Pressure\", kde= True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7931df97-4ea0-4490-b3d0-ce3f8b431fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(heart[heart['output'] == 0][\"chol\"], color='green',kde=True,) \n",
    "sns.distplot(heart[heart['output'] == 1][\"chol\"], color='red',kde=True)\n",
    "plt.title('Cholestrol versus Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9574bd8c-feb9-46b8-b12c-3eb267c19fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(heart[heart['output'] == 0][\"trtbps\"], color='green',kde=True,) \n",
    "sns.distplot(heart[heart['output'] == 1][\"trtbps\"], color='red',kde=True)\n",
    "plt.title('Trtbs versus Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd88abfa-1437-450e-8c7f-a24003cb1824",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(heart[heart['output'] == 0][\"thalachh\"], color='green',kde=True,) \n",
    "sns.distplot(heart[heart['output'] == 1][\"thalachh\"], color='red',kde=True)\n",
    "plt.title('Thalachh versus Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a3eced5-bdc8-41a5-8918-2c0dab40e1ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pair Plot\n",
    "\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.pairplot(heart)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbeb33f8-5249-460e-b791-c2988f70e44c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Violin Plot\n",
    "\n",
    "plt.figure(figsize=(13,13))\n",
    "plt.subplot(2,3,1)\n",
    "sns.violinplot(x = 'sex', y = 'output', data = heart)\n",
    "plt.subplot(2,3,2)\n",
    "sns.violinplot(x = 'thall', y = 'output', data = heart)\n",
    "plt.subplot(2,3,3)\n",
    "sns.violinplot(x = 'exng', y = 'output', data = heart)\n",
    "plt.subplot(2,3,4)\n",
    "sns.violinplot(x = 'restecg', y = 'output', data = heart)\n",
    "plt.subplot(2,3,5)\n",
    "sns.violinplot(x = 'cp', y = 'output', data = heart)\n",
    "plt.xticks(fontsize=9, rotation=45)\n",
    "plt.subplot(2,3,6)\n",
    "sns.violinplot(x = 'fbs', y = 'output', data = heart)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e9d31774-fb5e-451e-af82-c5c74aecd72c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[  1. ,   2. , 140. , ...,   0. ,   0. ,   1. ],\n",
       "        [  0. ,   3. , 160. , ...,   0. ,   1. ,   2. ],\n",
       "        [  1. ,   2. , 130. , ...,   0. ,   0. ,   1. ],\n",
       "        ...,\n",
       "        [  1. ,   4. , 130. , ...,   1. ,   1.2,   2. ],\n",
       "        [  0. ,   2. , 130. , ...,   0. ,   0. ,   2. ],\n",
       "        [  1. ,   3. , 138. , ...,   0. ,   0. ,   1. ]]),\n",
       " array([0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "        0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "        1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "        0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
       "        1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "        0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "        1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "        0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "        1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "        1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "        1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "        1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "        1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "        1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "        1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "        1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "        0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "        0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "        0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n",
       "        1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "        1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "        0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
       "        1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "        0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "        1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "        1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "        1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "        1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "        0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "        0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0], dtype=int64))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data preprocessing\n",
    "#There's no need for categorical encoding\n",
    "\n",
    "x = heart.iloc[:, 1:-1].values\n",
    "y = heart.iloc[:, -1].values\n",
    "x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "072976f9-25c3-40f6-89de-00cea0d8a861",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Splitting the dataset into training and testing data\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.2, random_state= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fdd4a2a3-b252-458d-bb26-6b9e45883eff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape for training data (734, 10) (734,)\n",
      "Shape for testing data (184, 10) (184,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape for training data', x_train.shape, y_train.shape)\n",
    "print('Shape for testing data', x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "580eb385-7518-485e-97ec-e8a9ba597e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling\n",
    "\n",
    "scaler = StandardScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8b9a520-7590-47c9-9887-a9ab94988dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.51740017,  0.81785035,  0.40398537, ...,  1.19939076,\n",
       "          1.0553738 ,  0.59527342],\n",
       "        [ 0.51740017,  0.81785035, -0.95050604, ...,  1.19939076,\n",
       "          0.57784676,  0.59527342],\n",
       "        [-1.93273999, -0.25411779,  1.48757849, ..., -0.83375663,\n",
       "         -0.0906911 , -1.03506497],\n",
       "        ...,\n",
       "        [-1.93273999,  0.81785035, -0.24617051, ..., -0.83375663,\n",
       "         -0.85473437, -1.03506497],\n",
       "        [ 0.51740017,  0.81785035,  0.07890743, ...,  1.19939076,\n",
       "          2.96548197,  2.22561181],\n",
       "        [ 0.51740017, -0.25411779, -1.32976364, ..., -0.83375663,\n",
       "         -0.85473437, -1.03506497]]),\n",
       " array([[ 0.51740017,  0.81785035, -0.95050604, ..., -0.83375663,\n",
       "         -0.75922896,  0.59527342],\n",
       "        [ 0.51740017,  0.81785035, -0.67960776, ..., -0.83375663,\n",
       "         -0.47271274,  0.59527342],\n",
       "        [ 0.51740017,  0.81785035, -1.22140432, ..., -0.83375663,\n",
       "         -0.85473437,  0.59527342],\n",
       "        ...,\n",
       "        [ 0.51740017,  0.81785035, -1.11304501, ..., -0.83375663,\n",
       "         -0.85473437, -1.03506497],\n",
       "        [ 0.51740017, -1.32608593, -0.1378112 , ..., -0.83375663,\n",
       "         -0.85473437, -1.03506497],\n",
       "        [ 0.51740017, -0.25411779,  1.48757849, ...,  1.19939076,\n",
       "         -0.85473437,  0.59527342]]))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train,x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f8ee048-4e59-456b-a3e2-1966f786db97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      " [[59 18]\n",
      " [13 94]]\n",
      "\n",
      "\n",
      "The accuracy of Logistic Regression is :  83.15217391304348 %\n"
     ]
    }
   ],
   "source": [
    "#1. Logistic Regression\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)\n",
    "predicted=model.predict(x_test)\n",
    "conf = confusion_matrix(y_test, predicted)\n",
    "print (\"Confusion Matrix : \\n\", conf)\n",
    "print()\n",
    "print()\n",
    "print (\"The accuracy of Logistic Regression is : \", accuracy_score(y_test, predicted)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c265e234-9d50-4457-a2b2-6aec8f354054",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Gaussian Naive Bayes model is :  83.15217391304348 %\n"
     ]
    }
   ],
   "source": [
    "#2.Gaussian Naive Bayes\n",
    "\n",
    "model = GaussianNB()\n",
    "model.fit(x_train, y_train)\n",
    "  \n",
    "predicted = model.predict(x_test)\n",
    "  \n",
    "print(\"The accuracy of Gaussian Naive Bayes model is : \", accuracy_score(y_test, predicted)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "93795644-25c1-4413-9346-76a1dca7de1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Gaussian Naive Bayes model is :  81.52173913043478 %\n"
     ]
    }
   ],
   "source": [
    "#3.Bernoulli Naive Bayes\n",
    "\n",
    "model = BernoulliNB()\n",
    "model.fit(x_train, y_train)\n",
    "  \n",
    "predicted = model.predict(x_test)\n",
    "  \n",
    "print(\"The accuracy of Gaussian Naive Bayes model is : \", accuracy_score(y_test, predicted)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "08c7f1c9-c7bc-4e1a-af0c-e6faeb9c0701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of SVM is :  84.78260869565217 %\n"
     ]
    }
   ],
   "source": [
    "#4.Support Vector Machine\n",
    "\n",
    "model = SVC()\n",
    "model.fit(x_train, y_train)\n",
    "  \n",
    "predicted = model.predict(x_test)\n",
    "print(\"The accuracy of SVM is : \", accuracy_score(y_test, predicted)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb001d1d-3099-41e9-aba2-be141538f423",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Random Forest is :  86.41304347826086 %\n"
     ]
    }
   ],
   "source": [
    "#5.Random Forest\n",
    "\n",
    "model = RandomForestRegressor(n_estimators = 100, random_state = 0)  \n",
    "model.fit(x_train, y_train)  \n",
    "predicted = model.predict(x_test)\n",
    "print(\"The accuracy of Random Forest is : \", accuracy_score(y_test, predicted.round())*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0eb97a96-f402-4b1a-b29e-fd33cc1de540",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63 14]\n",
      " [16 91]]\n",
      "The accuracy of KNN is :  83.69565217391305 %\n"
     ]
    }
   ],
   "source": [
    "#6.K Nearest Neighbours\n",
    "\n",
    "  \n",
    "model = KNeighborsClassifier(n_neighbors = 1)  \n",
    "model.fit(x_train, y_train)\n",
    "predicted = model.predict(x_test)\n",
    "  \n",
    "\n",
    "print(confusion_matrix(y_test, predicted))\n",
    "print(\"The accuracy of KNN is : \", accuracy_score(y_test, predicted.round())*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "98d88329-40ba-4d7e-8360-7594d32497a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Optimizing the KNN\n",
    "\n",
    "error_rate = []\n",
    "  \n",
    "for i in range(1, 40):\n",
    "      \n",
    "    model = KNeighborsClassifier(n_neighbors = i)\n",
    "    model.fit(x_train, y_train)\n",
    "    pred_i = model.predict(x_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))\n",
    "  \n",
    "plt.figure(figsize =(10, 6))\n",
    "plt.plot(range(1, 40), error_rate, color ='blue',\n",
    "                linestyle ='dashed', marker ='o',\n",
    "         markerfacecolor ='red', markersize = 10)\n",
    "  \n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "849108fc-a9af-40f4-9f23-d466adb96820",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[62 15]\n",
      " [13 94]]\n",
      "\n",
      "\n",
      "The accuracy of KNN is :  84.78260869565217 %\n"
     ]
    }
   ],
   "source": [
    "#With k=7 as it hovers after that\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors = 7)\n",
    "  \n",
    "model.fit(x_train, y_train)\n",
    "predicted = model.predict(x_test)\n",
    "  \n",
    "print('Confusion Matrix :')\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test, predicted))\n",
    "\n",
    "print()\n",
    "print()\n",
    "print(\"The accuracy of KNN is : \", accuracy_score(y_test, predicted.round())*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "689b0965-6d7c-497c-a1ad-854ba935a208",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The accuracy of X Gradient Boosting is :  84.23913043478261 %\n"
     ]
    }
   ],
   "source": [
    "#7.X Gradient Boosting\n",
    "\n",
    "model = xgb.XGBClassifier(use_label_encoder=False)\n",
    "model.fit(x_train, y_train)\n",
    "   \n",
    "predicted = model.predict(x_test)\n",
    "   \n",
    "cm = confusion_matrix(y_test, predicted)\n",
    "print()\n",
    "print (\"The accuracy of X Gradient Boosting is : \", accuracy_score(y_test, predicted)*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "46b58383-03e8-4072-984d-99184b7b9e86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convolutional Neural Network (CNN)\n",
      "ACCURACY\n",
      "86.41304347826086\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pavan\\AppData\\Local\\anaconda3\\envs\\tensor\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "print(\"Convolutional Neural Network (CNN)\")\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "mlpc = MLPClassifier().fit(x_train, y_train)\n",
    "y_pred = mlpc.predict(x_test)\n",
    "print(\"ACCURACY\")\n",
    "print(accuracy_score(y_test, y_pred) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cd9c6cba-5a25-40d9-b4d0-18e36cf898fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The patient is predicted to have heart disease.\n"
     ]
    }
   ],
   "source": [
    "random_index = random.randint(0, len(x_test) - 1)\n",
    "random_patient = x_test[random_index]\n",
    "predicted_result = mlpc.predict([random_patient])\n",
    "\n",
    "if predicted_result == 1:\n",
    "    print(\"The patient is predicted to have heart disease.\")\n",
    "else:\n",
    "    print(\"The patient is predicted not to have heart disease.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa992a41-b1df-4850-9a52-dc391fb3d71d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34e5d16e-48f5-43f8-a0d2-aeeaf2ce43a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}